{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  SYS_NAME     NAME  TARGET_ID DESCRIPTION ENTITY         VALUE COLLECTTIME\n",
      "0   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.427079e+07  2014-10-01\n",
      "1   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.026259e+07  2014-10-01\n",
      "2   财务管理系统  CWXT_DB        183        磁盘容量    C:\\  5.232332e+07  2014-10-01\n",
      "3   财务管理系统  CWXT_DB        183        磁盘容量    D:\\  1.572833e+08  2014-10-01\n",
      "4   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.432890e+07  2014-10-02\n",
      "            SYS_NAME     NAME  TARGET_ID DESCRIPTION ENTITY        VALUE\n",
      "COLLECTTIME                                                             \n",
      "2014-10-01    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  34270787.33\n",
      "2014-10-02    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  34328899.02\n",
      "2014-10-03    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  34327553.50\n",
      "2014-10-04    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  34288672.21\n",
      "2014-10-05    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  34190978.41\n",
      "            SYS_NAME     NAME  TARGET_ID DESCRIPTION ENTITY        VALUE\n",
      "COLLECTTIME                                                             \n",
      "2014-10-01    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  80262592.65\n",
      "2014-10-02    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  83200151.65\n",
      "2014-10-03    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  83208320.00\n",
      "2014-10-04    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  83099271.65\n",
      "2014-10-05    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  82765171.65\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "dataPath = './data/discdata.xls'\n",
    "\n",
    "data = pd.read_excel(dataPath,encoding='utf-8')\n",
    "\n",
    "#绘制C、D盘的使用情况时序图\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "#配置matplotlib参数\n",
    "#坐标轴字体\n",
    "matplotlib.rc('font', **{'family': 'serif', 'serif': ['SimHei']})\n",
    "#plt.rcParams['front.sans-serif'] = ['SimHei']\n",
    "#坐标轴负号\n",
    "#plt.rcParams['axes.unicode-minus'] = False\n",
    "\n",
    "pd.to_datetime(data['COLLECTTIME'])\n",
    "data1 = data[(data['ENTITY'] == 'C:\\\\') & (data['TARGET_ID'] == 184)]\n",
    "#设置dataframe索引，修改dataframe，不创建新对象\n",
    "data1.set_index('COLLECTTIME',inplace = True)\n",
    "\n",
    "data2 = data[(data['ENTITY'] == 'D:\\\\') & (data['TARGET_ID'] == 184)]\n",
    "data2.set_index('COLLECTTIME',inplace = True)\n",
    "\n",
    "print(data.head())\n",
    "print(data1.head())\n",
    "print(data2.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data1.index, data1['VALUE'], 'ro-')\n",
    "plt.title(u\"C盘已使用空间的时序图\")\n",
    "plt.xlabel(u'日期')\n",
    "plt.ylabel(u'磁盘使用大小')\n",
    "#刻度位置\n",
    "plt.xticks(rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data2.index,data2['VALUE'],'ko-')\n",
    "plt.title(u'D盘已使用空间的时序图')\n",
    "plt.xlabel(u'日期')\n",
    "plt.ylabel(u'磁盘使用大小')\n",
    "plt.xticks(rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(data.columns[:-1],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SYS_NAME     NAME  TARGET_ID DESCRIPTION ENTITY         VALUE COLLECTTIME\n",
      "0     财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.427079e+07  2014-10-01\n",
      "1     财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.026259e+07  2014-10-01\n",
      "2     财务管理系统  CWXT_DB        183        磁盘容量    C:\\  5.232332e+07  2014-10-01\n",
      "3     财务管理系统  CWXT_DB        183        磁盘容量    D:\\  1.572833e+08  2014-10-01\n",
      "4     财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.432890e+07  2014-10-02\n",
      "5     财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.320015e+07  2014-10-02\n",
      "8     财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.432755e+07  2014-10-03\n",
      "9     财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.320832e+07  2014-10-03\n",
      "12    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.428867e+07  2014-10-04\n",
      "13    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.309927e+07  2014-10-04\n",
      "16    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.419098e+07  2014-10-05\n",
      "17    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.276517e+07  2014-10-05\n",
      "20    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.418761e+07  2014-10-06\n",
      "21    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.252290e+07  2014-10-06\n",
      "24    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.428528e+07  2014-10-07\n",
      "25    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.259088e+07  2014-10-07\n",
      "28    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.429058e+07  2014-10-08\n",
      "29    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.236817e+07  2014-10-08\n",
      "32    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.321187e+07  2014-10-09\n",
      "33    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.217226e+07  2014-10-09\n",
      "36    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.324925e+07  2014-10-10\n",
      "37    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.192268e+07  2014-10-10\n",
      "40    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.325383e+07  2014-10-11\n",
      "41    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.484472e+07  2014-10-11\n",
      "44    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.432806e+07  2014-10-12\n",
      "45    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.476987e+07  2014-10-12\n",
      "48    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.432867e+07  2014-10-13\n",
      "49    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.455870e+07  2014-10-13\n",
      "52    财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.423493e+07  2014-10-14\n",
      "53    财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.420717e+07  2014-10-14\n",
      "..       ...      ...        ...         ...    ...           ...         ...\n",
      "128   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.546204e+07  2014-11-02\n",
      "129   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.645402e+07  2014-11-02\n",
      "132   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.548682e+07  2014-11-03\n",
      "133   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.612704e+07  2014-11-03\n",
      "136   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.547109e+07  2014-11-04\n",
      "137   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.616139e+07  2014-11-04\n",
      "140   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.554756e+07  2014-11-05\n",
      "141   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.593893e+07  2014-11-05\n",
      "144   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.560694e+07  2014-11-06\n",
      "145   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.564506e+07  2014-11-06\n",
      "148   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.554671e+07  2014-11-07\n",
      "149   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.527293e+07  2014-11-07\n",
      "152   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.551097e+07  2014-11-08\n",
      "153   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.811010e+07  2014-11-08\n",
      "156   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.549150e+07  2014-11-09\n",
      "157   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.812863e+07  2014-11-09\n",
      "160   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.560199e+07  2014-11-10\n",
      "161   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.807600e+07  2014-11-10\n",
      "164   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.568797e+07  2014-11-11\n",
      "165   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.775353e+07  2014-11-11\n",
      "168   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.570431e+07  2014-11-12\n",
      "169   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.724934e+07  2014-11-12\n",
      "172   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.570498e+07  2014-11-13\n",
      "173   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.698614e+07  2014-11-13\n",
      "176   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.457039e+07  2014-11-14\n",
      "177   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.667824e+07  2014-11-14\n",
      "180   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.467382e+07  2014-11-15\n",
      "181   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.976660e+07  2014-11-15\n",
      "184   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.479325e+07  2014-11-16\n",
      "185   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.937753e+07  2014-11-16\n",
      "\n",
      "[96 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            SYS_NAME  CWXT_DB:184:C:\\  CWXT_DB:184:D:\\ COLLECTTIME\n",
      "COLLECTTIME                                                       \n",
      "2014-10-01    财务管理系统      34270787.33      80262592.65  2014-10-01\n",
      "2014-10-02    财务管理系统      34328899.02      83200151.65  2014-10-02\n",
      "2014-10-03    财务管理系统      34327553.50      83208320.00  2014-10-03\n",
      "2014-10-04    财务管理系统      34288672.21      83099271.65  2014-10-04\n",
      "2014-10-05    财务管理系统      34190978.41      82765171.65  2014-10-05\n"
     ]
    }
   ],
   "source": [
    "data3 = pd.DataFrame(index = data1.index,columns=['SYS_NAME','CWXT_DB:184:C:\\\\','CWXT_DB:184:D:\\\\','COLLECTTIME'])\n",
    "data3['SYS_NAME'] = data1['SYS_NAME']\n",
    "data3['CWXT_DB:184:C:\\\\'] = data1['VALUE']\n",
    "data3['CWXT_DB:184:D:\\\\'] = data2['VALUE']\n",
    "data3['COLLECTTIME'] = data1.index\n",
    "data3.to_excel('./data/data_processed.xls')\n",
    "print(data3.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#自相关与偏相关图\n",
    "#原始数据\n",
    "import statsmodels.api as sm\n",
    "\n",
    "dta = data3['CWXT_DB:184:C:\\\\']\n",
    "fig = plt.figure(figsize = (12,12))\n",
    "ax1 = fig.add_subplot(411)\n",
    "fig = sm.graphics.tsa.plot_acf(dta, lags = 40,ax = ax1)\n",
    "ax1.set_title(u'原始数据的自相关图')\n",
    "\n",
    "ax2 = fig.add_subplot(412)\n",
    "fig = sm.graphics.tsa.plot_pacf(dta, lags = 40, ax = ax2)\n",
    "ax2.set_title(u'原始数据的偏自相关图')\n",
    "\n",
    "#一阶差分后去空值取自相关系数\n",
    "dta = dta.diff(1).dropna() \n",
    "ax3 = fig.add_subplot(413)\n",
    "fig = sm.graphics.tsa.plot_acf(dta, lags = 40, ax = ax3)\n",
    "ax3.set_title(u'一阶差分的自相关图')\n",
    "\n",
    "ax4 = fig.add_subplot(414)\n",
    "fig = sm.graphics.tsa.plot_pacf(dta, lags = 40, ax = ax4)\n",
    "ax4.set_title(u'一阶差分后的偏自相关图')\n",
    "\n",
    "plt.savefig('./data/acf_pacf.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列经过1阶差分后归于平稳，p值为0.0010121052494076637\n"
     ]
    }
   ],
   "source": [
    "#ADF检验\n",
    "#参数初始化\n",
    "discfile = './data/data_processed.xls'\n",
    "\n",
    "data4 = pd.read_excel(discfile)\n",
    "#不使用最后5个数据\n",
    "data = data4.iloc[:len(data)-5]\n",
    "\n",
    "#平稳性测试\n",
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "diff = 0 \n",
    "adf = ADF(data['CWXT_DB:184:C:\\\\'])\n",
    "#print(adf)\n",
    "#adf[1]为p值，p值小于0.05认为是平稳的\n",
    "while adf[1] >= 0.05:\n",
    "    diff = diff + 1\n",
    "    adf = ADF(data['CWXT_DB:184:C:\\\\'].diff(diff).dropna())\n",
    "    #print(adf)\n",
    "    \n",
    "print(u'原始序列经过%s阶差分后归于平稳，p值为%s'%(diff,adf[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列为非白噪声序列，对应的p值为：1.848096385405586e-07\n",
      "一阶差分为白噪声序列，对应的p值为：0.49124089427835793\n"
     ]
    }
   ],
   "source": [
    "#白噪声检验\n",
    "#LB统计量\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "[[lb],[p]] = acorr_ljungbox(data['CWXT_DB:184:C:\\\\'], lags=1)\n",
    "if p < 0.05:\n",
    "    print(u'原始序列为非白噪声序列，对应的p值为：%s'%p)\n",
    "else:\n",
    "    print(u'原始序列为白噪声序列，对应的p值为：%s'%p)\n",
    "    \n",
    "[[lb],[p]] = acorr_ljungbox(data['CWXT_DB:184:C:\\\\'].diff(1).dropna(),lags=1)\n",
    "if p < 0.05:\n",
    "    print(u'一阶差分序列为非白噪声序列，对应的p值为：%s'%p)\n",
    "else:\n",
    "    print(u'一阶差分为白噪声序列，对应的p值为：%s'%p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\pythonenv\\datamining\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "E:\\pythonenv\\datamining\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:634: RuntimeWarning: invalid value encountered in log\n",
      "  invarcoefs = -np.log((1-params)/(1+params))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC最小的p值和q值为:0、0\n"
     ]
    }
   ],
   "source": [
    "#模型识别\n",
    "#确定最佳p、d、q值\n",
    "#xdata = data['CWXT_DB:184:D:\\\\']\n",
    "xdata = data['CWXT_DB:184:C:\\\\']\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "#定阶\n",
    "pmax = int(len(xdata)/10) #一般阶数不超过length/10\n",
    "qmax = int(len(xdata)/10)\n",
    "bic_matrix = [] #bic矩阵\n",
    "for p in range(pmax+1):\n",
    "    tmp = []\n",
    "    for q in range(qmax+1):\n",
    "        try:\n",
    "            tmp.append(ARIMA(xdata,(p,1,q)).fit().bic)\n",
    "        except:\n",
    "            tmp.append(None)\n",
    "    bic_matrix.append(tmp)\n",
    "bic_matrix = pd.DataFrame(bic_matrix) #取值区域\n",
    "#stack()将数据from columns to indexs\n",
    "p,q = bic_matrix.stack().astype('float64').idxmin()\n",
    "print(u'BIC最小的p值和q值为:%s、%s'%(p,q))\n",
    "#D:1,1\n",
    "#C:0,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型ARIMA（0,1,0）符合白噪声检验\n"
     ]
    }
   ],
   "source": [
    "#模型检验\n",
    "lagnum = 12\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "arima = ARIMA(xdata,(p,1,q)).fit()\n",
    "xdata_pred = arima.predict(typ = 'levels')#predict\n",
    "#print(xdata_pred)\n",
    "pred_error = (xdata_pred - xdata).dropna()#残差\n",
    "\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "lb,p_l = acorr_ljungbox(pred_error, lags = lagnum)\n",
    "h = (p_l < 0.05).sum()#p值小于0.05，认为是非白噪声\n",
    "if h > 0:\n",
    "    print(u'模型ARIMA（%s,1,%s）不符合白噪声检验'%(p,q))\n",
    "else:\n",
    "    print(u'模型ARIMA（%s,1,%s）符合白噪声检验'%(p,q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35644056.49277778 35681171.87555556 35718287.25833334 35755402.64111112\n",
      " 35792518.0238889 ]\n",
      "           实际容量         预测容量\n",
      "42  35704312.58  35644056.49\n",
      "43  35704980.73  35681171.88\n",
      "44  34570385.45  35718287.26\n",
      "45  34673820.69  35755402.64\n",
      "46  34793245.31  35792518.02\n"
     ]
    }
   ],
   "source": [
    "#模型预测\n",
    "#forecast向前预测5个值\n",
    "test_predict = arima.forecast(5)[0]\n",
    "print(test_predict)\n",
    "\n",
    "#预测对比\n",
    "test_data = pd.DataFrame(columns = [u'实际容量',u'预测容量'])\n",
    "test_data[u'实际容量'] = data4[(len(data4)-5):]['CWXT_DB:184:C:\\\\']\n",
    "test_data[u'预测容量'] = test_predict\n",
    "test_data = test_data.applymap(lambda x :'%.2f'%x)\n",
    "print(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42    0.060256\n",
      "43    0.023809\n",
      "44    1.147902\n",
      "45    1.081582\n",
      "46    0.999273\n",
      "dtype: float64\n",
      "平均绝对误差为：0.6626,\n",
      "均方根误差为：0.9822,\n",
      "平均绝对百分误差为：0.000000。\n"
     ]
    }
   ],
   "source": [
    "#计算误差\n",
    "#列操作\n",
    "test_data[u'预测容量'] = test_data[u'预测容量'].astype(float)\n",
    "test_data[u'实际容量'] = test_data[u'实际容量'].astype(float)\n",
    "#10**6单位换算\n",
    "abs_ = (test_data[u'预测容量'] - test_data[u'实际容量']).abs()/10**6\n",
    "mae_ = abs_.mean()\n",
    "rmse_ = ((abs_**2).mean())**0.05\n",
    "mape_ = (abs_/test_data[u'实际容量']).mean()\n",
    "\n",
    "print(u'平均绝对误差为：%0.4f,\\n均方根误差为：%0.4f,\\n平均绝对百分误差为：%0.6f。'%(mae_,rmse_,mape_))"
   ]
  }
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